About

Fred Chong is the Seymour Goodman Professor in the Department of Computer Science at the University of Chicago and serves as the Chief Scientist for Quantum Software at ColdQuanta. He is the Lead Principal Investigator for the EPiQC Project (Enabling Practical-scale Quantum Computing), an NSF Expedition in Computing, which aims to develop new algorithms, software, and machine designs tailored to quantum device technologies with 100 to 1000 quantum bits. Chong is also a member of the National Quantum Advisory Committee (NQIAC), providing advice to the President and Secretary of Energy on the National Quantum Initiative Program. Chong received his Ph.D. from MIT in 1996 and has held faculty positions at UC Davis and UCSB, where he was a Professor of Computer Science, Director of Computer Engineering, and Director of the Greenscale Center for Energy-Efficient Computing. His research interests include emerging technologies for computing, quantum computing, multicore and embedded architectures, computer security, and sustainable computing. He has been funded by numerous agencies and industry partners, leading or co-leading over $40 million in research awards. His work in quantum computing focuses on bridging the gap between theoretical algorithms and practical architectures, with the goal of enabling scientific discoveries and advancing high-performance computing. Chong has received multiple awards, including the NSF CAREER award, the Intel Outstanding Researcher Award, and 13 best paper awards.

Research topics

• Computer Science
• Physics
• Quantum mechanics
• Artificial Intelligence
• Theoretical computer science
• Algorithm
• Computer network
• Mathematics
• Engineering
• Electronic engineering
• Computer engineering
• Mathematical optimization
• Distributed computing
• Data science
• History
• Library science
• Telecommunications
• Database
• Combinatorics

Selected publications

• Erasure Minesweeper: Exploring Hybrid-Erasure Surface Code Architectures for Efficient Quantum Error Correction

  2025-08-30

  articleSenior author

  Dual-rail erasure qubits can substantially improve the efficiency of quantum error correction, allowing lower error rates to be achieved with fewer qubits, but each erasure qubit requires <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \times$</tex> more transmons to implement compared to standard qubits. In this work, we introduce a hybrid-erasure architecture for surface code error correction where a carefully chosen subset of qubits is designated as erasure qubits while the rest remain standard. Through code-capacity analysis and circuitlevel simulations, we show that a hybrid-erasure architecture can boost the performance of the surface code-much like how a game of Minesweeper becomes easier once a few squares are revealed-while using fewer resources than a full-erasure architecture. We study strategies for the allocation and placement of erasure qubits through analysis and simulations. We then use the hybrid-erasure architecture to explore the trade-offs between per-qubit cost and key logical performance metrics such as threshold and effective distance in surface code error correction. Our results show that the strategic introduction of dual-rail erasure qubits in a transmon architecture can enhance the logical performance of surface codes for a fixed transmon budget, particularly for near-term-relevant transmon counts and logical error rates.

  PublisherDOI

• Matching Generalized-Bicycle Codes to Neutral Atoms for Low-Overhead Fault-Tolerance

  2025-08-30 · 1 citations

  articleSenior author

  As quantum machines have scaled up in their number of qubits, significant research has turned towards increasing their fidelity with quantum error correction codes. Although promising results have been shown with the surface code, which only requires near-neighbor connections between qubits, the high qubit overhead of such local codes promises to be problematic. Consequently, recent work has explored non-local codes such as quantum LDPC (qLDPC) codes, which have good asymptotic encoding rates. Despite good theoretical progress, hardware implementations of non-local qLDPC codes has been a longstanding challenge. At the experimental level, recent demonstrations of movement based communication on neutral atom arrays suggest this is a powerful new primitive to achieve non-local connectivity. Leveraging this, we present a protocol for implementing non-local qLDPC codes in hardware. Our protocol, qSIEVE, is a co-design of such codes with movement in atom arrays. qSIEVE defines a restricted family of qLDPC codes that can be implemented efficiently with systolic movement. qSIEVE enables codes for qubit storage with reduced over-heads of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10 x$</tex> compared to the surface code while maintaining comparable error-correcting performance. Measuring a round of stabilizers in qSIEVE is also 5-11x faster than existing protocols for non-local codes in atom arrays. qSIEVE is also scalable, and we propose a tiled memory design with shared controls to adapt qSIEVE to larger program sizes.

  PublisherDOI

• Roadblocks and Opportunities in Quantum Algorithms -- Insights from the National Quantum Initiative Joint Algorithms Workshop, May 20--22, 2024

  ArXiv.org · 2025-08-19

  articleOpen access

  The National Quantum Initiative Joint Algorithms Workshop brought together researchers across academia, national laboratories, and industry to assess the current landscape of quantum algorithms and discuss roadblocks to progress. The workshop featured discussions on emerging algorithmic techniques, resource constraints in near-term hardware, and opportunities for co-design across software and systems. Presented here are seven topics from the workshop, each highlighting a critical challenge or promising opportunity discussed during the event. Together, they offer a snapshot of the field's evolving priorities and a shared vision for what is needed to advance quantum computational capabilities.

  PublisherOA PDF

• Enhancing Chemistry on Quantum Computers with Fermionic Linear Optical Simulation

  ArXiv.org · 2025-11-16

  preprintOpen access

  We present and open source a quantum circuit simulator tailored to chemistry applications. More specifically, our simulator can compute the Born-rule probabilities of samples obtained from circuits containing passive fermionic linear optical elements and controlled-phase gates. We support both approximate and exact calculation of probabilities, and for approximate probability calculation, our simulator's runtime is exponential only in the magnitudes of the circuit's controlled-phase gate angles. This makes our simulator useful for simulating certain systems that are beyond the reach of conventional state vector methods. We demonstrate our simulator's utility by simulating the local cluster unitary Jastrow (LUCJ) ansatz and integrating it with sample-based quantum diagonalization (SQD) to improve the accuracy of molecular ground-state energy estimates. Applied to a 52-qubit $N_2$ system, we observe accuracy improvements of up to $46\%$ over the baseline SQD implementation with negligible computational overhead. More generally, we highlight a regime in which our simulator achieves substantially superior latency scaling and exponentially superior memory scaling over a tensor network simulator and a state vector simulator. As an efficient and flexible tool for simulating quantum chemistry circuits, our simulator enables new opportunities for enhancing near-term quantum algorithms in chemistry and related domains.

  PublisherOA PDFDOI

• Abstract 5020: Quantum algorithms improve feature selection for multimodal cancer classification

  Cancer Research · 2025-04-21

  article

  Introduction: Cancer machine learning research is often limited by overparameterization and overfitting, which arise because cancer ‘omic’ variables significantly outnumber patient samples. Traditional feature selection methods for multimodal cancer data fail to completely capture complex biological relationships across genomic, transcriptomic, and pathomic data due in part to computational intractability—interaction information for 3rd-order and higher terms scale exponentially, infeasible for classical methods. We developed a novel quantum-classical hybrid algorithm to identify small, informative feature sets that incorporate higher-order correlations, potentially revealing previously undetected biological signals to improve cancer classification. Methods: We implemented a quantum-classical hybrid algorithm that frames feature selection as a polynomial constrained binary optimization problem (PCBO-Tournament). Our approach leverages quantum resources through the Recursive Quantum Approximate Optimization Algorithm to explore higher-order feature correlations. We evaluated the algorithm on The Cancer Genome Atlas (TCGA) pan-squamous cell carcinoma cohort (lung, cervical, and head and neck) (1,427 patients) with DNA mutational data, mRNA expression data, and pathomics data for tissue-of-origin classification. To reduce noise and enable efficient data loading, we binarized DNA single-nucleotide polymorphisms (2,720 loci), discretized normalized mRNA expression values into quantiles (18,879 genes), and processed pathomics data with the Slideflow platform, using the Prov-GigaPath foundation model to generate slide-level features (2,048) that we then discretized into quartiles. Feature sets were assessed in terms of the balanced accuracy of logistic regression models in cancer sub-type classification. Results: On the TCGA pan-squamous dataset, our quantum-enhanced algorithm selected feature sets of size 14 that achieved higher balanced accuracy in cancer classification (97%, P&amp;lt;0.01), compared to both models containing all features and those with classically selected feature sets. The algorithm identified biologically relevant features, including tumor suppressor genes such as TP53, along expression of tissue-specific markers (SFTPA1/A2/B), developmental regulators (TBX4/5), and epigenetic factors (HIRIP3). The selected features demonstrated robust performance across data modalities, with a variant of our full-qubo PCBO outperforming classical approaches in the small feature set regime (N≤20). Conclusion: Our quantum-enhanced PCBO feature selection method identifies small, biologically meaningful feature sets that outperform classical approaches, particularly in terms of incorporating higher-order correlations. This work illustrates the potential for quantum computing to advance multimodal biomarker discovery and precision oncology. Citation Format: Siddhi Ramesh, Teague Tomesh, Ryan Robinett, Dmitry Karpeyev, Colin Campbell, Bharath Thotakura, Frederic Chong, Samantha Riesenfeld, Alexander Pearson. Quantum algorithms improve feature selection for multimodal cancer classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5020.

  PublisherDOI

• Modeling Short-Range Microwave Networks to Scale Superconducting Quantum Computation

  Quantum · 2025-01-08 · 7 citations

  articleOpen accessSenior author

  A core challenge for superconducting quantum computers is to scale up the number of qubits in each processor without increasing noise or cross-talk. Distributed quantum computing across small qubit arrays, known as chiplets, can address these challenges in a scalable manner. We propose a chiplet architecture over microwave links with potential to exceed monolithic performance on near-term hardware. Our methods of modeling and evaluating the chiplet architecture bridge the physical and network layers in these processors. We find evidence that distributing computation across chiplets may reduce the overall error rates associated with moving data across the device, despite higher error figures for transfers across links. Preliminary analyses suggest that latency is not substantially impacted, and that at least some applications and architectures may avoid bottlenecks around chiplet boundaries. In the long-term, short-range networks may underlie quantum computers just as local area networks underlie classical datacenters and supercomputers today.

  PublisherOA PDFDOI

• qSIEVE: Efficient qLDPC Memory via Systolic Movement in Atom Arrays

  ACM Transactions on Quantum Computing · 2025-12-12

  articleOpen accessSenior author

  As quantum machines have scaled up in their number of qubits, significant research has turned towards increasing their fidelity with quantum error correction codes. Although promising results have been shown with the surface code, which only requires near-neighbor connections between qubits, the high qubit overhead of such local codes promises to be problematic. Consequently, recent work has explored non-local quantum LDPC (qLDPC) codes, which have good asymptotic encoding rates. Despite theoretical progress, hardware implementations of these codes have been a longstanding challenge. At the experimental level, demonstrations of movement based communication on atom arrays suggest this is a powerful new primitive to achieve non-local connectivity. Leveraging this, we present a protocol for implementing non-local qLDPC codes in hardware. Our protocol, qSIEVE, is a co-design of such codes with movement in atom arrays. qSIEVE defines a restricted family of qLDPC codes that can be implemented efficiently with systolic movement. We then quantify the utility of qSIEVE in the context of a complete fault tolerant architecture. We compare the cost of implementing benchmark programs in a standard, surface code only architecture and a mixed architecture where data is stored in qLDPC memory with qSIEVE and loaded to surface codes for computation.

  PublisherDOI

• Erasure Minesweeper: exploring hybrid-erasure surface code architectures for efficient quantum error correction

  ArXiv.org · 2025-04-30

  preprintOpen accessSenior author

  Dual-rail erasure qubits can substantially improve the efficiency of quantum error correction, allowing lower error rates to be achieved with fewer qubits, but each erasure qubit requires $3\times$ more transmons to implement compared to standard qubits. In this work, we introduce a hybrid-erasure architecture for surface code error correction where a carefully chosen subset of qubits is designated as erasure qubits while the rest remain standard. Through code-capacity analysis and circuit-level simulations, we show that a hybrid-erasure architecture can boost the performance of the surface code -- much like how a game of Minesweeper becomes easier once a few squares are revealed -- while using fewer resources than a full-erasure architecture. We study strategies for the allocation and placement of erasure qubits through analysis and simulations. We then use the hybrid-erasure architecture to explore the trade-offs between per-qubit cost and key logical performance metrics such as threshold and effective distance in surface code error correction. Our results show that the strategic introduction of dual-rail erasure qubits in a transmon architecture can enhance the logical performance of surface codes for a fixed transmon budget, particularly for near-term-relevant transmon counts and logical error rates.

  PublisherOA PDFDOI

• k-Contextuality as a Heuristic for Memory Separations in Learning

  ArXiv.org · 2025-07-15

  preprintOpen access

  Classical machine learning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. Previously, the existence of a long-range correlational structure known as contextuality was shown to inhibit efficient classical machine learning representations of certain quantum-inspired sequential distributions. Here, we define a new quantifier of contextuality we call strong k-contextuality, and prove that any translation task exhibiting strong k-contextuality is unable to be represented to finite relative entropy by a classical streaming model with fewer than k latent states. Importantly, this correlation measure does not induce a similar resource lower bound for quantum generative models. Using this theory as motivation, we develop efficient algorithms which estimate our new measure of contextuality in sequential data, and empirically show that this estimate is a good predictor for the difference in performance of resource-constrained classical and quantum Bayesian networks in modeling the data. Strong k-contextuality thus emerges as a measure to help identify problems that are difficult for classical computers, but may not be for quantum computers.

  PublisherOA PDFDOI

• $k$-Contextuality as a Heuristic for Memory Separations in Learning

  2025-08-30

  article

  Classical machine learning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. Previously, the existence of a long-range correlational structure known as contextuality was shown to inhibit efficient classical machine learning representations of certain quantum-inspired sequential distributions. Here, we define a new quantifier of contextuality we call strong <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-contextuality, and prove that any translation task exhibiting strong <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-contextuality is unable to be represented to finite relative entropy by a classical streaming model with fewer than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> latent states. Importantly, this correlation measure does not induce a similar resource lower bound for quantum generative models. Using this theory as motivation, we develop efficient algorithms which estimate our new measure of contextuality in sequential data, and empirically show that this estimate is a good predictor for the difference in performance of resource-constrained classical and quantum Bayesian networks in modeling the data. Strong <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-contextuality thus emerges as a measure to help identify problems that are difficult for classical computers, but may not be for quantum computers.

  PublisherDOI

Recent grants

• ITR: Synchroscalar: Exploiting Synchronized Clock Domains for Energy Efficient Multirate Embedded Computation

  NSF · $312k · 2003–2007

• Collaborative Research: EPiQC: Enabling Practical-Scale Quantum Computation

  NSF · $5.3M · 2018–2024

• Collaborative Research: PIF: Scalable Quantum Computers in the Presence of Physical Noise: a Study of Surface Codes with Realistic Errors at the Algorithmic Level

  NSF · $234k · 2016–2018

• Collaborative Research: CT-T: A Vertical Systems Framework for Effective Defense against Memory-Based Attacks

  NSF · $278k · 2006–2011

Frequent coauthors

• Pranav Gokhale

  82 shared

• Jonathan M. Baker

  79 shared

• Gokul Subramanian Ravi

  60 shared

• Kaitlin N. Smith

  52 shared

• Yongshan Ding

  44 shared

• Tzvetan S. Metodi

  Sandia National Laboratories

  43 shared

• Timothy Sherwood

  University of California, Santa Barbara

  40 shared

• Diana Franklin

  University of Chicago

  37 shared

Labs

• EPiQCPI

Education

• Ph.D.

  MIT

  1996

• Other

  UC Davis

• Other

  UCSB