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Trapped Ion Quantum Information

CHRISTOPHER MONROE, Principal Investigator. University of Maryland Department of Physics, Joint Quantum Institute, and Center for Quantum Information and Computer Science

University of Maryland

Engineering a Control System for a Logical Qubit-Scale Trapped Ion Quantum Computer, Andrew Risinger, Ph.D Electrical and Computer Engineering (2022)

Non-Integrable Dynamics in a Trapped-Ion Quantum Simulator , Patrick Becker, Ph.D. Physics (2021)

Simulating many-body quantum spin models with trapped ions , Antonis Kyprianidis, Ph.D. Physics (2021)

Experimental Study of Quantum Algorithms on Ion-trap Quantum Computers , Daiwei Zhu, Ph.D. Electrical and Computer Engineering (2021)

Design and Construction of a Three-Node Quantum Network , Allison Carter, Ph.D Physics (2021)

Scaling Quantum Computers with Long Chains of Trapped Ions , Laird Egan, Ph.D. Physics (2021)

Cryogenic Trapped-Ion System for Large-Scale Quantum Simulation , Wen Lin Tan, Ph.D.Physics (2021)

Mixed-Species Ion Chains for Quantum Networks , Ksenia Sosnova, Ph.D. Physics (2020)

Construction, Optimization, and Applications of a Small Trapped-Ion Quantum Computer , Kevin Landsman, Ph.D. Electrical Engineering (2019)

High Purity Single Photons Entangled with Barium Ions for Quantum Networking , Clayton Crocker, Ph.D. Physics (2019)

Building and Programming a Universal Ion Trap Quantum Computer , Caroline Figgatt, Ph.D. Physics (2018)

Demonstration of a Quantum Gate with Ultrafast Laser Pulses , Jamie David Wong Campos, Ph.D. Physics (2017)

A Scanning Transfer Cavity Frequency Lock for Experimental Quantum Information , Katherine S. Collins, B.S. Physics (2017).

Manipulation of the Quantum Motion of Trapped Atomic Ions via Stimulated Raman Transitions , Kenneth Wright, Ph.D. Physics (2017)

Multi-Species Trapped Atomic Ion Modules for Quantum Networks , I. Volkan Inlek, Ph.D. Physics (2016)

A Programmable Five Qubit Quantum Computer using Trapped Atomic Ions , Shantanu Debnath, Ph.D. Physics (2016).

Experiments with Trapped Ions and Ultrafast Laser Pulses , Kale Johnson, Ph.D. Physics (2016).

Quantum Thermalization and Localization in a Trapped Ion Quantum Simulator , Jacob Smith, Ph.D. Physics (2016)

Engineering a Quantum Many-Body Hamiltonian with Trapped Ions, Aaron C. Lee, Ph.D. Physics (2016)

A Modular Quantum System of Trapped Atomic Ions, David Alexander Hucul, Ph.D. Physics (2015)

Dynamics and Excited States of Quantum Many-Body Spin Chains with Trapped ions , C. R. Senko, Ph.D. Physics (2014).

Quantum information processing with trapped ion chains , T. Andrew Manning, Ph.D. Physics (2014).

Ultrafast Control of Spin and Motion in Trapped Ions , Jonathan Albert Mizrahi, Ph.D. Physics (2013).

Quantum Simulations of the Ising Model: Devil’s Staircase and Arbitrary Lattice Proposal , Simcha Korenblit, Ph.D. Physics (2013).

Remote and Local Entanglement of Ions with Photons and Phonons , David Lee Hayes, Ph.D. Physics (2012).

Quantum Simulation of Interacting Spin Models with Trapped Ions , Kazi Rajibul Islam, Ph.D. Physics (2012).

Ytterbium Ion Qubit State Detection on an ICCD Camera , Aaron Lee, B.S. Physics (2012).

Enhanced Light Collection from Single Trapped Ions , Jonathan Sterk, Ph.D. Physics (2011).

State Detection of a Trapped Ion Qubit Using Photon Arrival Times , Kenny W Lee, B.S. Physics (2011).

Quantum Teleportation Between Distant Matter Qubits , Steven Olmschenk, Ph.D. Physics (2009).

Doppler-Free Spectroscopy of Iodine at 739nm , Andrew Chew, B.S. Physics (2008).

University of Michigan

Detection and Control of Individual Trapped Ions and Neutral Atoms , Mark Acton, Ph.D. Physics (2008).

Implementation of Grover’s Quantum Search Algorithm with Two Trapped Ions , Kathy-Anne Brickman, Ph.D. Physics (2007).

Remote Entanglement of Trapped Atomic Ions , David Moehring, Ph.D. Physics (2007).

Fabrication and Characterization of Semiconductor Ion Traps for Quantum Information Processing , Daniel Stick, Ph.D. Physics (2007).

Advanced Ion Trap Development and Ultrafast Laser-Ion Interactions , Martin Madsen, Ph.D. Physics (2006).

Operation of a Two-Dimensional Ion Trap Array for Scalable Quantum Computation , David Hucul, B.S. Physics (2006).

The Design and Implementation of Atomic Ion Shuttling Protocols in a Multi-Dimensional Ion Trap Array , Mark Yeo, B.S. Physics (2006).

Controlled Coherent Excitations in a Single Cadmium Ion with an Ultrafast Laser , R. N. Kohn, Jr., B. S. Physics (2006).

Cooling and Heating of the Quantum Motion of Trapped Cadmium Ions , Louis Deslauriers, Ph.D. Physics (2006).

Quantum Information Processing with Two Trapped Cadmium Ions , Patricia Lee, Ph.D. Physics (2005).

An Apparatus for the Observation of Trapped Cadmium Ion Interactions with Intense Laser Pulses , Russell Miller, B.S. Physics (2003).

Theses on the topic of quantum computing

Available Topics

Currently, all thesis topics are already assigned. New theses will be announced by mid-2024.

Already assigned topics

Stochastic gradient line bayesian optimization (sglbo) for quantum computing (b.sc.).

Current algorithms for quantum computers are based on so-called variational approaches. In this process, quantum gates - the programmable building blocks of quantum computers - are parameterized with variables and numerically optimized using conventional computers. To do this, gradient-based methods are often applied. However, noise poses a particular challenge when it comes to optimization due to the probabilistic nature of quantum physics, as well as the significant measurement noise of today’s error-prone hardware. Optimization methods for quantum algorithms must therefore be able to deal with noise.

This is where machine learning optimization methods come into play. A highly promising optimization approach is Stochastic Gradient Line Bayesian Optimization (SGLBO). This uses a machine learning method (Bayesian Optimization) to control the optimization step by step. A recent publication demonstrated how this can give quantum algorithms an advantage over other optimization methods. This bachelor thesis will examine to what extent quantum neural networks can be optimized using the SGLBO method. Quantum neural networks function like artificial neural networks, but they are run on a quantum computer. In the first part of the thesis, the SGLBO method will be implemented and tested in Python. This will be followed by a comparison with other previously-implemented optimization methods under different noise influences.  Finally, the thesis will assess how well the optimization works on IBM's real quantum computing hardware. The thesis provides an exciting opportunity to address current optimization challenges using quantum computing and to make an important contribution in this field. Prior knowledge of numerical optimization is a great advantage, as well as a general interest in the topics of quantum computing and machine learning.

Quantum Kernel Methods for Solving Differential Equations (B.Sc, M.Sc)

In the DEGRAD-EL3-Q project, we are investigating how quantum computing methods can be used to analyze the lifetime of electrolyzers. The project is part of the lead project H2Giga and aims at advancing the industrial manufacturing process of electrolyzers. The mathematical description of how electrolyzers behave in operation can be modeled by differential equations. In this project, we want to explore the extent to which quantum kernel methods can be used to solve differential equations. In addition, a systematic comparison will be made with the quantum neural networks also studied in the project. This is an exciting and forward-looking topic in the superposition state of quantum computing and hydrogen research.

Evaluating the Use of Quantum Graph Neural Networks to Predict Molecular Properties (M.Sc.)

Scalable and cost-effective solutions for storing renewable energy are essential if we are to meet the world's increasing energy demand and simultaneously mitigate climate change. The conversion of electricity to hydrogen, as well as the reverse combustion process, can play an important role. To make catalysis processes in hydrogen production efficient, new materials are constantly being studied. Machine learning methods are already being used to simulate and calculate catalysis properties. Graph-based neural networks (GNN) are proving to be particularly promising in this respect. Since the prediction of potential surfaces and other relevant properties takes place at molecular and atomic level, the use of quantum computers is also being considered. First approaches to implement GNNs on quantum computers have already been published. The objective of the master thesis is to determine the suitability of quantum GNNs for predicting molecular properties. To this end, depending on prior knowledge, an understanding of GNNs, as well as some basic knowledge of quantum computing, must first be acquired. In-depth knowledge of electrocatalysis is not necessarily required. Towards the end of the master thesis, the developed approaches can be tested and evaluated on a real quantum computer.

Error Mitigation and Detection in Circuit Quantum Electrodynamics Powered by QND Measurements, Jacob Charles Curtis, Ph.D. (2023). PDF

Improving the Coherence of Superconducting Quantum Circuits through Loss Characterization and Design Optimization, Suhas Ganjam, Ph.D. (2023) PDF

Error Detection in Bosonic Circuit Quantum Electrodynamics, James Teoh, Ph.D. (2023) PDF

Piezo-Brillouin Electro-Optomechanics with High-Overtone Bulk Acoustic Resonators, Taekwan Yoon, Ph.D. (2022) PDF

Thin-film 3D Resonators for Superconducting Quantum Circuits, Lev Krayzman, Ph.D. (2022) PDF

Bosonic Quantum Simulation in Circuit Quantum Electrodynamics , Christopher S. Wang, Ph.D. (2022) PDF

Error-Detected Networking for 3D Circuit Quantum Electrodynamics, Luke D. Burkhart, Ph.D. (2020) PDF

Controlling Error-Correctable Bosonic Qubits, Philip Reinhold, Ph.D. (2019) PDF

Building Blocks for Modular Circuit QED Quantum Computing, Christopher James Axline, Ph.D. (2018). to order, visit LULU.com, PDF

Teleported operations between logical qubits in circuit quantum electrodynamics, Kevin Chou, Ph.D. (2018) PDF

Multi-Cavity Operations in Circuit Quantum Electrodynamics , Yvonne Y. Gao, Ph.D. (2018) PDF

Multiqubit Experiments in 3D Circuit Quantum Electrodynamics, (to order, visit LULU.com ), Jacob Blumoff, Ph.D. (2017) PDF

Micromachined Quantum Circuits , (to order, visit LULU.com ), Teresa Brecht, Ph.D. (2017) PDF

Enhancing the Lifetime of Quantum Information with Cat States in Superconducting Cavities , Andrei A. Petrenko, Ph.D. (2016) PDF

Superconducting Cavities for Circuit Quantum Electrodynamics , Matthew Reagor, Ph.D. (2015) PDF

Controlling Coherent State Superpositions with Superconducting Circuits, Brian Vlastakis, Ph.D. (2015) PDF

Cavity State Reservoir Engineering in Circuit Quantum Electrodynamics, Eric Holland, Ph.D. (2015) PDF

Circuit Quantum Electrodynamics with Electrons on Helium, Andreas Fragner, Ph.D. (2013) PDF

Extending Coherence in Superconducting Qubits: from Microseconds to Milliseconds, Adam Sears, Ph.D. (2013) PDF

Entanglement and Quantum Error Correction with Superconducting Qubits, Matthew Reed, Ph.D. (2013) PDF

Controlling Photons in Superconducting Electrical Circuits, Blake Johnson, Ph.D. (2011) PDF

Quantum Information Processing with Superconducting Qubits , Jerry Chow, Ph.D. (2010) PDF

Superconducting Tunnel Junctions as Direct Detectors for Submillimeter Astronomy, John Teufel, Ph.D. (2008)   PDF

Resolved Dynamics of Single Electron Tunneling Using the RF-SET , Julie Love, Ph.D. (2007) PDF

Circuit Quantum Electrodynamics , David Schuster, Ph.D. (2007) PDF

Precision Measurements with the Single Electron Transistor: Noise and Backaction in the Normal and Superconducting State, Benjamin Turek, Ph.D. (2007) PDF

Design and Fabrication of Superconducting Circuit for Amplification and Processing of Quantum Signal , Luigi Frunzio, Ph.D. (2006) PDF

The Shot Noise Thermometer (to order a book, go to LULU.com , ​Lafe Spietz, Ph.D. (2006) PDF

Low Temperature Electron-Phonon Interaction in Disordered Metal Thin Films and Applications to Fast, Sensitive Sub-Millimeter Photon Sources and Detectors , Minghao Shen, Ph.D. (2005) PDF

Undergraduate Senior Theses

Automated extraction of single-qubit gate errors via randomized benchmarking   (Senior Thesis) 

Peter J. Karalekas (2015), PDF

Design of Microwave Splitter/Combiner for Use in cQED Experiments (Senior Thesis) 

Jared Schwede (2007),  PDF

Quantum Computing

Quantum Computing merges two great scientific revolutions of the 20th century: computer science and quantum physics. Quantum physics is the theoretical basis of the transistor, the laser, and other technologies which enabled the computing revolution. But on the algorithmic level, today's computing machinery still operates on ""classical"" Boolean logic. Quantum Computing is the design of hardware and software that replaces Boolean logic by quantum law at the algorithmic level. For certain computations such as optimization, sampling, search or quantum simulation this promises dramatic speedups. We are particularly interested in applying quantum computing to artificial intelligence and machine learning. This is because many tasks in these areas rely on solving hard optimization problems or performing efficient sampling.

Recent Publications

Some of our teams.

Applied science

We're always looking for more talented, passionate people.

Houck Lab Quantum computing and condensed matter physics with microwave photons

Ph.D. theses from the Houck group

Circuit quantum electrodynamics (cQED) serves as a promising platform for scalable quantum computation, where precise microwave control of qubits lays the foundation for achieving high-fidelity quantum gates. Despite recent progress in developing various quantum gates, controlling artificial qubits remains a considerable challenge due to intricate Hamiltonian systems and the fragile nature of quantum states. Therefore, further research is needed to improve qubit gates and protect quantum states from qubit decoherence. This thesis presents two studies: 1) controlling cQED systems through black-box optimization to achieve state-of-the-art gate fidelity, 2) stabilizing an entangled two-qubit state indefinitely via engineering dissipation channels. The first study establishes the feasibility of direct black-box optimization as a method to discover novel qubit gates from simple initial conditions. We develop robust quantum optimization algorithms to efficiently learn novel qubit gates and evaluate these algorithms through simulations and experiments. Our findings show the potential to learn high-fidelity qubit gates without depending on the specifics of the system Hamiltonian. In the second study, our objective is to realize entanglement stabilization through quantum reservoir engineering. By coupling two qubits near resonance with a leaky resonator acting as a reservoir, we induce a strong correlated decay of the qubits. We experimentally demonstrate the subradiant effect of an entangled Bell state and, through simulation, reveal the robustness of this system in stabilizing a high-fidelity Bell state.

The potential for quantum computing to expand the number of solvable problems has driven researchers across academia and industry, in multiple disciplines, to develop a variety of different qubit platforms, algorithms, and scaling strategies. At its core, quantum computation relies on the robustness, or coherence, of its building blocks (qubits"). In current small-scale superconducting qubit processors, the fidelity of operations is often limited by qubit coherence. The coherence time of a single qubit depends on its lifetime $T_1$ and pure dephasing time $T_{\phi}$. In this thesis, we focus on the problem of improving $T_1$. Strategies for improving lifetimes are informed by models for relaxation - specifically Fermi's Golden Rule. Relaxation rates depend on noise properties of the environment and on properties of the qubit states. This dependence suggests two strategies for engineering longer lifetimes: environment engineering involves mitigating or filtering the noise that the qubit sees, and Hamiltonian engineering refers to optimizing the qubit circuit and its resulting eigenstates to optimize $T_1$. Significant enhancements of qubit lifetimes will require paradigm shifts in our approaches to both environment and Hamiltonian engineering. First, I present a side-by-side study of transmon coherence and materials measurements of the constituent Nb films, including synchrotron x-ray spectroscopy and electron microscopy. We found correlations between qubit lifetimes and materials properties such as grain size, grain boundary quality, and surface suboxides. This study expands the scope of superconducting qubit research by presenting a broad set of materials analyses alongside device measurements. Second, I will give an overview of Hamiltonian engineering, including the concepts behind intrinsic protection against relaxation and dephasing processes. I'll describe the soft $\mathrm{0-\pi}$ qubit, which is the first experimentally realized superconducting qubit to show signatures of simultaneous $T_1$ and $T_2$ protection. We improved coherence in the soft $\mathrm{0-\pi}$ through optimized fabrication processes. We have also characterized the effects of non-computational levels on gate fidelity, specifically AC Stark shifts and leakage. From the results in this thesis, we have gained a deeper understanding of what limits qubit coherence, informing future directions on both the materials and Hamiltonian engineering fronts.

A useful quantum computer requires a full stack of components, where each layer in the stack can actually scale. In this thesis we go through each layer of the quantum computing stack, from the bottom to the top. First, we discuss planar tantalum transmon qubit fabrication. We iterate on the design and fabrication of an entangling gate module with two fixed-frequency transmon qubits and a tunable coupler. We share our perspective on making a robust parametric entangling gate architecture for planar superconducting qubits. Next, we introduce the QICK (Quantum Instrumentation Control Kit), which is a standalone open source controller for both superconducting and atomic qubits as well as various detectors. Highly integrated open source firmware and software has been designed to allow the QICK to scale to hundreds of qubits. We develop the QICK for the superconducting qubit platform and use it to conduct the first single and multi-qubit experiments. Finally, we develop two modular simulation frameworks---one for a multinode quantum computer, and one for heterogeneous qubit architectures.

Superconducting quantum circuits are a promising platform for quantum computation. The building block for most quantum processors is a qubit (quantum bit) which can store information in a superposition of two states. Superconducting qubits are lithographically defined from metals, often niobium or aluminum. However, these devices have limited use because the information they store decays before most useful computations can take place. In this thesis we explore the cause of these losses. Specifically, we employ tantalum as the capacitor pad of a two-dimensional transmon qubit and find lifetimes and coherence times with dynamical decoupling over 300 us. We then switch to a resonator geometry to probe tantalum materials properties. We develop a power and temperature dependent measurement to quantify sources of decay. We find our resonators are primarily limited by two-level system loss at materials interfaces. Finally we employ this resonator characterization method to determine the effects of processing treatments and new packages onresonator decay, showing a buffered-oxide etch before measurement reduces two-level system loss.

Over the past decade, quantum circuits have been transitioning from being useful solely in fundamental physics research to having applications in a wide variety of fields. This has been made possible by the advancements in the coherence, coupling and optimal control of various elements of these quantum circuits. The experiments presented in this thesis solve critical challenges for the above mentioned areas. We provide the first experimental realization of a protected qubit having simultaneous robustness to relaxation and dephasing processes. We show a 40-fold improvement in the coherence time in fluxonium qubit by harnessing insights from Floquet engineering. Furthermore, we also demonstrate a coupling architecture for suppressing qubit-qubit crosstalk. The above works unlock new directions for improving the state of quantum systems

In recent years, superconducting circuits have become a promising architecture for quantum computing and quantum simulation. This advancing technology offers excellent scalability, long coherence times, and large photon nonlinearities, making it a versatile platform for studying non-equilibrium condensed matter physics with light. This thesis covers a series of experiments and theoretical developments aimed at probing strongly correlated states of interacting photons. Building upon previous efforts on nonlinear superconducting lattices, this work focuses on establishing new platforms for generating interactions between microwave photons in multi-mode circuits. The first experiment presents a new paradigm in exploiting the nonlinearity of a Josephson junction to tailor the Hilbert space of harmonic oscillators using a dynamical three-wave mixing process. This allows a single microwave resonator to be addressed as a two-level system, offering a promising pathway to long-lived qubits. A theoretical proposal is outlined for building a field-programmable quantum simulator, harnessing this dynamical nonlinearity for stimulating strong photon-photon interactions. The system consists of a lattice of harmonic modes in synthetic dimensions, where particle hopping and on-site interactions can be independently controlled via frequency-selective flux modulation. Numerical studies show that for strong interactions the driven-dissipative steady-state develops a crystalline phase for photons. The second experiment explores the physics of quantum impurities, where a single well-controlled qubit is coupled to the many modes of a photonic crystal waveguide. The light-matter coupling strength is pushed into the ultrastrong coupling regime, where the qubit is simultaneously hybridized with many modes and the total number of excitations is not conserved. Probing transport through the waveguide reveals that the propagation of a single photon becomes a many-body problem as multi-photon bound states participate in the scattering dynamics. Furthermore, the effective photon interactions induced by just this single impurity leads to interesting inelastic emission of photons. Probing correlations in the field emission reveals signatures of multi-mode entanglement. This work presents opportunities for exploring large-scale lattices with strongly interacting photons. These platforms are compatible with well-established techniques for generating artificial magnetic fields and stabilizing many-body states through reservoir engineering, complementing growing efforts in the quest for building synthetic quantum materials with light.

Over the past 10 years, improvements to the fundamental components in supercon- ducting qubits and the realization of novel circuit topologies have increased the life- times of qubits and catapulted this architecture to become one of the leading hardware platforms for universal quantum computation. Despite the progress that has been made in increasing the lifetime of the charge qubit by almost six orders of magnitude, further improvements must be made to climb over the threshold for fault tolerant quantum computation. Two complimentary approaches towards achieving this goal are investigating and improving upon existing qubit designs, and looking for new types of superconducting qubits which would offer some intrinsic improvements over existing designs. This thesis will explore both of these directions through a detailed study of new materials, circuit designs, and coupling schemes for superconducting qubits. In the first experiment, we explore the use of disordered superconducting films, specifically Niobium Titanium Nitride, as the inductive element in a fluxonium qubit and measure the loss mechanisms limiting the qubit lifetime. In the second experiment, we work towards the experimental realization of the 0 − π qubit archi- tecture, which offers the promise of intrinsic protection in lifetime and decoherence compared to existing superconducting qubits. In the final experiment, we design and measure a two qubit device where the static σz ⊗ σz crosstalk between the two qubits is eliminated via destructive interference. The use of multiple coupling elements re- moves the σz ⊗ σz crosstalk while maintaining the large σz ⊗ σx interaction needed to perform two qubit gates.

Electrical and Computer Engineering

College of engineering.

Preparing for Quantum Computing

By Giordana Verrengia

• kristab(through)cmu.edu

While quantum computers are prototypical as of today, a security measure called post-quantum cryptography (PQC) is already in use — some notable examples being the Google Chrome browser and the internet giant Cloudflare.

Researchers from Carnegie Mellon University, Graz University of Technology in Austria, and Tallinn University of Technology in Estonia have collaborated to identify vulnerabilities in PQC. Their work — which looks at Dilithium, an electronic signature algorithm — is part of a concerted effort among industry professionals to beat the clock and develop a reliable PQC algorithm before quantum computers become readily available at least 10 years down the line.

Sam Pagliarini , a special professor of electrical and computer engineering, says there are key differences between applications of classical and quantum computers. The classical devices we use now, like laptops and desktops, will not be replaced. Quantum computers — which are designed to excel at complex calculations — will be used almost exclusively for research purposes in higher education and government settings to solve problems related to mathematics, physics, and chemistry.  

Given that quantum devices will be hard to access, why is post-quantum cryptography so important, and why is it currently in use?

Because of a tactic called “store now, decrypt later”: Hackers harvest encrypted data in hopes of acquiring the necessary decryption tools later. Data can be swiped from a classical device and decrypted later with a quantum computer, underscoring the need for industry and government figures to work ahead and introduce a standardized PQC algorithm well before the devices are built.

“PQC isn’t science fiction. It’s serious in the sense that the US government has a mandate in place for every federal agency to switch to a form of communication that is secure against quantum computers . For some, the deadline is as soon as 2025,” Pagliarini says. 

One way to test if PQC algorithms are up to the challenge involves ethical hacking. Pagliarini and his fellow researchers created an algorithm called REPQC to identify any security vulnerabilities when Dilithium is implemented as a computer chip. Dilithium’s lattice-based algorithm structure is important to probe because it was chosen by the National Institute of Standards and Technology for standardization as experts work to advance PQC. Using reverse engineering, the team inserted a hardware trojan horse (HTH) that used reverse engineering to locate where sensitive data was stored on the hardware accelerator. The team developed additional circuitry that leaked a secret key, which decrypts data and could be used to forge signatures.

“My entire motivation is to find weak spots and bring attention to them,” says Pagliarini. “This research is mostly about protection against a new class of devices, quantum computers, while not losing sight of threats that exist today, such as reverse engineering.”

The multi-university team’s paper, “REPQC: Reverse Engineering and Backdooring Hardware Accelerators for Post-quantum Cryptography,” was accepted to the prestigious 19th ACM ASIA Conference on Computer and Communications Security taking place in Singapore from July 1-5, 2024.

Related People

• Sam Pagliarini
• Official White House Announcement

Researchers Take The High-Dimensional Road to Scalable Quantum Computers

• Research , Uncategorized

Matt Swayne

August 24, 2024.

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Insider Brief

• Researchers developed a new way to tap high-dimensional quantum states, potentially leading to scalable quantum computers and communication systems.
• One of the key innovations is the use of qudits, which can encode more information and reduce system complexity compared to traditional qubits.
• The technique involves Raman-assisted two-photon interactions, enabling precise manipulation of these higher-dimensional states, paving the way for more efficient quantum operations.

Researchers report they developed a new approach to quantum computing using high-dimensional quantum states that might one day lead to quantum computers and communication systems that are scalable.

The team, published their findings in Nature , report that the key to their scheme is a quantum unit that operates beyond the traditional binary states of qubits — or qudits — which can encode more information and reduce system complexity.

Traditional quantum computing relies on bits of quantum information known as “qubits,” similar to the bits in classical computing, according to the team of researchers, which include researchers from the University of California, Berkeley, Lawrence Berkeley National Laboratory and Korea University, However, qubits face limitations due to hardware constraints such as fabrication imperfections and restricted connectivity. Qudits, on the other hand, exploit additional energy levels within the system and that allow them to encode more information and reduce complexity.

The following might grossly oversimplify the researchers’ work, but the light switch on your wall might offer at least a basic idea of higher dimensionality and the advantages of qudits compared to qubits. Imagine a qubit as a simple light switch, which can only be either on or off—that would be two possible states. If a problem requires checking multiple states, you would need multiple light switches (qubits) and that can quickly grow complicated and cumbersome as the problem expands.

Now, imagine a qudit as a dimmer switch that not only turns the light on or off but can also adjust the light to multiple levels of brightness. This dimmer switch — qudit — can be set to several different positions (states), not just on or off. So, with a single qudit, you can hold and process more information than with a single qubit, in other words, higher dimensionality.

The researchers successfully demonstrated the manipulation of these high-dimensional states using an approach that relies on a technique involving Raman-assisted two-photon interactions. For simplicity’s sake, this technique can be broken down into a two-step process. First, Raman scattering is used, where light interacts with a material to change its energy levels. This is then combined with two-photon absorption, where two photons are absorbed simultaneously to shift the quantum state. This allows the researchers to precisely manipulate the higher-dimensional states of qudits, which, the researchers suggest, can help scale quantum operations.

Methodology and Innovations

The core of the research lies in the experimental setup for superconducting circuits—one of the leading approaches scientists are using to develop quantum computers. These circuits behave as nonlinear harmonic oscillators, enabling the encoding of multiple quantum states within each qudit. By intricately connecting these circuits, or “gears,” and controlling them individually, the team crafted a quantum array capable of intricate operations and high-fidelity multi-qudit gates.

This setup allowed the team to produce atomic squeezed states and Schrödinger cat states—complex quantum states that are fundamental for quantum computing and information processing. Moreover, the put forward a novel theoretical framework for understanding two-photon dynamics in such multi-qudit systems and that could add new ways to investigate more efficient designs and applications in quantum sensing and fault-tolerant quantum computing.

Implications and Future Directions

This research could have implications in quantum computing and beyond, the team writes. By integrating the operational principles of simpler qubit systems with the expansive potential of high-dimensional quantum states, the team has shown that it is possible to enhance the performance and scalability of quantum devices. According to the paper, the methodology not only supports the creation of more sophisticated quantum networks but also bolsters their resilience against noise—a perennial challenge in quantum computing.

Looking ahead, the researchers hope that their findings will invigorate further studies into high-dimensional quantum systems. Their work suggests that these systems could be important to realizing practical quantum computing and complex quantum simulation models. Additionally, the protocol is adaptable, so it could be applied to other quantum platforms, potentially broadening the scope of quantum technologies.

The Team Behind the Breakthrough

The research was led by Dr. Long B. Nguyen and his colleagues Noah Goss, Karthik Siva, Bingcheng Qing, Akel Hashim, and David I. Santiago at the University of California, Berkeley, with further computational support from the Lawrence Berkeley National Laboratory. The team also included contributions from Yosep Kim of Korea University, highlighting the collaborative nature of this international research effort.

For more detailed information about the study and access to the full results, readers are encouraged to refer to the team’s publication in Nature .

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New Classical Algorithm Enhances Understanding of Quantum Computing’s Future

In an exciting development for quantum computing, researchers from the University of Chicago’s Department of Computer Science , Pritzker School of Molecular Engineering , and Argonne National Laboratory have introduced a groundbreaking classical algorithm that simulates Gaussian boson sampling (GBS) experiments. This achievement not only helps clarify the complexities of current quantum systems but also represents a significant step forward in our understanding of how quantum and classical computing can work together. The research just appeared in the prominent Nature Physics Journal this past June.

The Challenge of Gaussian Boson Sampling

Gaussian boson sampling has gained attention as a promising approach to demonstrating quantum advantage, meaning the ability of quantum computers to perform tasks that classical computers cannot do efficiently. The journey leading up to this breakthrough has been marked by a series of innovative experiments that tested the limits of quantum systems. Previous studies indicated that GBS is challenging for classical computers to simulate under ideal conditions. However, Assistant Professor and author Bill Fefferman pointed out that the noise and photon loss present in actual experiments create additional challenges that require careful analysis.

Notably, experiments ( such as these ) conducted by teams at major research centers from the University of Science and Technology of China and Xanadu, a Canadian quantum company, have shown that while quantum devices can produce outputs consistent with GBS predictions, the presence of noise often obscures these results, leading to questions about the claimed quantum advantage. These experiments served as a foundation for the current research, driving scientists to refine their approaches to GBS and better understand its limitations.

Understanding Noise in Quantum Experiments

“While the theoretical groundwork has established that quantum systems can outperform classical ones, the noise present in actual experiments introduces complexities that require rigorous analysis,” explained Fefferman. “​​Understanding how noise affects performance is crucial as we strive for practical applications of quantum computing.”

This new algorithm addresses these complexities by leveraging the high photon loss rates common in current GBS experiments to provide a more efficient and accurate simulation. The researchers employed a classical tensor-network approach that capitalizes on the behavior of quantum states in these noisy environments, making the simulation more efficient and manageable with available computational resources.

Breakthrough Results

Remarkably, the researchers found that their classical simulation performed better than some state-of-the-art GBS experiments in various benchmarks.

“What we’re seeing is not a failure of quantum computing, but rather an opportunity to refine our understanding of its capabilities,” Fefferman emphasized. “It allows us to improve our algorithms and push the boundaries of what we can achieve.”

The algorithm outperformed experiments by accurately capturing the ideal distribution of GBS output states, raising questions about the claimed quantum advantage of existing experiments. This insight opens doors for improving the design of future quantum experiments, suggesting that enhancing photon transmission rates and increasing the number of squeezed states could significantly boost their effectiveness.

Implications for Future Technologies

The implications of these findings extend beyond the realm of quantum computing. As quantum technologies continue to evolve, they hold the potential to revolutionize fields such as cryptography, materials science, and drug discovery. For instance, quantum computing could lead to breakthroughs in secure communication methods, enabling more robust protection of sensitive data. In materials science, quantum simulations can help discover new materials with unique properties, paving the way for advancements in technology, energy storage, and manufacturing. By advancing our understanding of these systems, researchers are laying the groundwork for practical applications that could change the way we approach complex problems in various sectors.

The pursuit of quantum advantage is not just an academic endeavor; it has tangible implications for industries that rely on complex computations. As quantum technologies mature, they have the potential to play a crucial role in optimizing supply chains, enhancing artificial intelligence algorithms, and improving climate modeling. The collaboration between quantum and classical computing is crucial for realizing these advancements, as it allows researchers to harness the strengths of both paradigms.

A Cumulative Research Effort

Fefferman worked closely with Professor Liang Jiang from the Pritzker School of Molecular Engineering and former postdoc Changhun Oh , currently an Assistant Professor at the Korea Advanced Institute of Science and Technology, on previous work that culminated in this piece of research.

In 2021, they examined the computational power of noisy intermediate-scale quantum (NISQ) devices through lossy boson sampling . The paper revealed that photon loss affects classical simulation costs depending on the number of input photons, which could lead to exponential savings in classical time complexity. Following this, their second paper focused on the impact of noise in experiments designed to demonstrate quantum supremacy, showing that even with significant noise, quantum devices can still produce results that are difficult for classical computers to match. In their third article, they explored Gaussian boson sampling (GBS) by proposing a new architecture that improves programmability and resilience against photon loss, making large scale experiments more feasible. They then introduced a classical algorithm in their fourth paper that generates outcomes closely aligned with ideal boson sampling, enhancing benchmarking techniques and emphasizing the importance of carefully selecting experiment sizes to preserve the quantum signal amidst noise.Finally, in their latest study, they developed quantum-inspired classical algorithms to tackle graph-theoretical problems like finding the densest k-subgraph and the maximum weight clique and a quantum chemistry problem called the molecular vibronic spectra generation . Their findings suggested that the claimed advantages of quantum methods may not be as significant as previously thought, with their classical sampler performing similarly to the Gaussian boson sampler.

Looking Ahead

The development of the classical simulation algorithm not only enhances our understanding of Gaussian boson sampling experiments but also highlights the importance of continued research in both quantum and classical computing. The ability to simulate GBS more effectively serves as a bridge toward more powerful quantum technologies, ultimately helping us navigate the complexities of modern challenges. As we explore these insights, we move closer to realizing the full potential of quantum technologies, which could lead to innovative solutions that benefit society as a whole. Each step forward in this journey brings us closer to a future where quantum computing plays a vital role in addressing some of the world’s most pressing challenges. With ongoing research and collaboration, the future of quantum computing looks promising, unlocking new realms of possibility for science and society.

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The IBM Quantum System One, pictured at Rensselaer Polytechnic Institute in upstate New York, is the first computer of its kind to be housed on a college campus.

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Universities Embrace Quantum Computing

Chris Hayhurst is a freelance writer who covers education technology and healthcare, among other topics. He's a regular contributor to the CDW family of technology magazines

Rensselaer Polytechnic Institute has a long-standing reputation as a leader among scientific and technological research universities. And now, as of April, it has another feather in its cap: RPI is the first college anywhere to host an IBM quantum computer.

The university, in upstate New York, unveiled the device — the IBM Quantum System One — in a computer center that once served as a Catholic community chapel. The system is housed in an enormous glass cube just a few feet away from a wall of stained glass windows.

“It’s a wonderful juxtaposition of old and new, of theology and technology,” says John Kolb , vice president of information services and technology and CIO for RPI.

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The system required significant calibration to get it up and running to its current stable state, so it was deployed with an uninterruptible power supply to prevent possible damage in the event of a power outage. One critical area for study is how problems that were previously solved with the help of an off-campus supercomputing center could use the new capabilities of the quantum computer. With that in mind, Kolb’s team installed a fiber broadband network connection between the two sites.

Kolb notes that the university offers a slate of courses on everything from quantum mechanics to nonlinear and quantum optics, and RPI researchers are investigating topics such as quantum entanglement and its applications in machine learning. The IBM Quantum System One, he says, will allow those who are interested across the campus community to get qubit-level, hands-on experience with cutting-edge quantum hardware and software.

Now that RPI has this machine, Kolb explains, its students have quantum technologies at their fingertips. “But the best part about it is how early we are in quantum computing itself. If you’re looking at this in terms of a career, opportunities for quantum experts in the future are going to be practically endless.”

Higher Education Institutions Prepare for a Quantum Future

Kolb isn’t alone in his interest in the potential for quantum computing to find new ways to solve interdisciplinary questions. A number of analysts agree with his assessment, and a small but growing collection of other universities across the country are also bringing quantum technologies to their campuses.

A recent report from McKinsey states that quantum computers “are poised to take computing to a whole new level.” The technology is still a work in progress, the authors note, but startup companies are investing heavily in the market, and established players such as Google and IBM are already offering cloud-based quantum computing services .

In the higher ed space, says McKinsey partner Saurabh Sanghvi, many institutions are taking a “multidisciplinary approach” to quantum computing, embracing the technology as a tool for researchers while developing new courses on its applications for everything from healthcare to engineering.  

“They’re teaching the technical skills to the computer science and physics students who will need to have quantum computing fluency,” he explains. “But they also see its relevance to those in other fields, such as business and law and the basic sciences.”  

That’s the case at the University of Maryland , which offers a graduate certificate program in quantum computing for working professionals with no prior experience in quantum technologies. At last count, the institution had eight quantum-focused facilities and supported the work of more than 200 quantum researchers.

RELATED: Find out how else higher education is integrating quantum computing.

UMD’s latest addition to its quantum portfolio is the National Quantum Laboratory , a research center established through a partnership with IonQ , which is headquartered in College Park. The QLab, as it’s also known, offers students and others affiliated with the university or its partners free access to IonQ machines. The lab also provides connectivity to other quantum systems through a regional consortium called the Mid-Atlantic Quantum Alliance .

Currently, notes Franz Klein, a high-performance computing engineer who serves as director of QLab, around 50 undergraduate and 20 graduate students are leveraging the facility for their studies. The IonQ systems are housed in a “clean room” down the hall from a collaboration space equipped with large-RAM workstations. Students use these computers to log in to the quantum machines through a connection to the IonQ Quantum Cloud .

“With a quantum system, you’re manipulating individual atoms, so you need highly stable environmental conditions,” Klein explains. “There can’t be any interference or fluctuations in temperature or humidity, which means you can’t work with them directly.”

One recent project at the lab explored how a quantum convolutional neural network, a type of model used in machine learning, might improve computational efficiency. Another investigated the use of quantum random number generation to bolster cybersecurity . “We’re just getting started here,” Klein says. “I think we have a lot to look forward to.”

John Kolb Vice President of Information Services at Technology and CIO, Rensselaer Polytechnic Institute

Howard University Aims to Expand Quantum Computing Access

Also focused on the future while nurturing student development in the here and now is Howard University in Washington, D.C. In 2020, Howard partnered with IBM to bring a quantum computing research facility to its campus through an initiative for historically Black colleges and universities.

The first of what today has become a long list of member institutions that together constitute the IBM-HBCU Quantum Center , the Howard program offers opportunities to students with all levels of experience in quantum technologies.

“When they get started, most of them have little to no background in quantum,” says Su Yan, who directs the center with help from program managers Michelai Lowe and Sherri Chandler. Interested undergraduates typically begin with a series of self-paced online courses on quantum computing fundamentals. From there, many progress to more challenging work, including research projects led by faculty and graduate students. Users access the program’s quantum machines over the cloud with their personal devices, and they rely on IBM’s Qiskit open-source software to create and manipulate quantum algorithms.

At the moment, Yan notes, Howard students and researchers are pursuing a wide variety of quantum projects, from the development of novel quantum materials for use in engineering applications to the integration of artificial intelligence with quantum technologies.

Lowe, who focuses on student and faculty engagement , says providing access to the IBM quantum systems “is just one piece of what we do.” The program also runs math and coding boot camps, for example, and it provides scholarship, fellowship, and internship opportunities for undergraduate and graduate students. Today, about 40 students are conducting research through the center, up from just a handful at launch four years ago.

“We have students from across disciplines,” Lowe says, including math, computer science, engineering, physics and biology. “At first, we were looking for students and faculty to participate, but now people are finding us.”

Much of that is due to recent advances in quantum computing and the fact that the subject is hot, she notes. It also helps that Howard President Ben Vinson III, who was named to the position in November 2023, included quantum computing on a list of “innovative research areas” he’s directing the university to pursue.

“It sounds cliché, but one of the things that we hope to accomplish is making sure that HBCUs have a seat at the table with everything that’s happening with the quantum boom,” Lowe says. “We think it’s important that we’re part of the conversation and the curriculum building that’s going to shape the future.”

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