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Fred Chong

Publications and source records attributed to Fred Chong.

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Spacetime-Efficient and Hardware-Compatible Complex Quantum Logic Units in qLDPC Codes

Quantum low-density parity-check (qLDPC) codes offer a promising route to scalable fault-tolerant quantum computing due to their substantially reduced footprint. However, these gains can be diluted at utility scale if we cannot also realize space-time efficient logical operations for relevant quantum applications. We present RASCqL, a reaction-time-limited architecture for space-time efficient complex-instruction-set quantum computation with qLDPC logic. RASCqL supports key algorithmic subroutines such as quantum arithmetic and state preparation directly within co-designed qLDPC codes, achieving $2\times$ to $7\times$ reductions in qubit footprint while maintaining space-time volume comparable to state-of-the-art transversal surface-code architectures. Unlike prior approaches that aim for versatile logical instruction sets for arbitrary circuits, RASCqL adopts an application-tailored code modification that embeds specific complex Clifford transformations useful for common subroutines as virtually implementable operations arising from code automorphisms. RASCqL further leverages parallel physical operations available in reconfigurable neutral-atom arrays to enable fast QEC cycles and high-fidelity transversal operations. At the cost of increased design complexity and specialization, RASCqL can improve end-to-end resource estimates for applications such as factoring and quantum chemistry simulation in both footprint and space-time volume under realistic physical error rates of approximately $2\times10^{-3}$ to $5\times10^{-4}$, without requiring additional hardware capabilities. These results demonstrate that qLDPC codes can serve as complex quantum logic units for useful quantum algorithms, extending their practical utility in fault-tolerant quantum computing architectures.

quant-ph

5 Year Update to the Next Steps in Quantum Computing

It has been 5 years since the Computing Community Consortium (CCC) Workshop on Next Steps in Quantum Computing, and significant progress has been made in closing the gap between useful quantum algorithms and quantum hardware. Yet much remains to be done, in particular in terms of mitigating errors and moving towards error-corrected machines. As we begin to transition from the Noisy-Intermediate Scale Quantum (NISQ) era to a future of fault-tolerant machines, now is an opportune time to reflect on how to apply what we have learned thus far and what research needs to be done to realize computational advantage with quantum machines.

cs.ET

Approximate t-designs in generic circuit architectures

Unitary t-designs are distributions on the unitary group whose first t moments appear maximally random. Previous work has established several upper bounds on the depths at which certain specific random quantum circuit ensembles approximate t-designs. Here we show that these bounds can be extended to any fixed architecture of Haar-random two-site gates. This is accomplished by relating the spectral gaps of such architectures to those of 1D brickwork architectures. Our bound depends on the details of the architecture only via the typical number of layers needed for a block of the circuit to form a connected graph over the sites. When this quantity is independent of width, the circuit forms an approximate t-design in linear depth. We also give an implicit bound for nondeterministic architectures in terms of properties of the corresponding distribution over fixed architectures.

quant-ph

Accelerating Progress Towards Practical Quantum Advantage: The Quantum Technology Demonstration Project Roadmap

Quantum information science and technology (QIST) is a critical and emerging technology with the potential for enormous world impact and is currently invested in by over 40 nations. To bring these large-scale investments to fruition and bridge the lower technology readiness levels (TRLs) of fundamental research at universities to the high TRLs necessary to realize the promise of practical quantum advantage accessible to industry and the public, we present a roadmap for Quantum Technology Demonstration Projects (QTDPs). Such QTDPs, focused on intermediate TRLs, are large-scale public-private partnerships with a high probability of translation from laboratory to practice. They create technology demonstrating a clear 'quantum advantage' for science breakthroughs that are user-motivated and will provide access to a broad and diverse community of scientific users. Successful implementation of a program of QTDPs will have large positive economic impacts.

quant-ph

NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms

Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. Some works consider the physical meaning of the underlying circuits, while others adopt the ideas of neural architecture search (NAS) for ansatz generator. However, these designs do not exploit the full advantages of VQAs. Because most techniques target gate ansatz, and the parameters are usually rotation angles of the gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on superconducting qubits. These control pulses need elaborate calibrations to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose NAPA, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed NAPA, we are tuning parametric pulses, which are natively supported on NISQ computers. Given the limited availability of gradient-based optimizers for pulse-level quantum programs, we choose to deploy non-gradient optimizers in our framework. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices for Variational Quantum Eigensolver (VQE) tasks to evaluate our methods.

quant-ph