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Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method.
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Quantum-inspired logic for advanced Transcriptional Programming
Abstract The tenets of intelligent biological systems are (i) scalable decision-making, (ii) inheritable memory, and (iii) communication. This study aims to increase the complexity of decision-making operations beyond standard Boolean logic, while minimizing the metabolic burden imposed on the chassis cell. To this end, we present a new platform technology for constructing genetic circuits with multiple OUTPUT gene control using fewer INPUTs relative to conventional genetic circuits. Inspired by principles from quantum computing, we engineered synthetic bidirectional promoters, regulated by synthetic transcription factors, to construct 1-INPUT, 2-OUTPUT logical operations—i.e. biological QUBIT and PAULI-X logic gates—designed as compressed genetic circuits. We then layered said gates to engineer additional quantum-inspired logical operations of increasing complexity—e.g. FEYNMAN and TOFFOLI gates. In addition, we engineered a 2-INPUT, 4-OUTPUT quantum operation to showcase the capacity to utilize the entire permutation INPUT space. Finally, we developed a recombinase-based memory operation to remap the truth table between two disparate logic gates—i.e. converting a QUBIT operation to an antithetical PAULI-X operation in situ. This study introduces a novel and versatile synthetic biology toolkit, which expands the biocomputing capacity of Transcriptional Programming via the development of compressed and scalable multi-INPUT/OUTPUT logical operations.
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- PAR ID:
- 10591997
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Nucleic Acids Research
- Volume:
- 53
- Issue:
- 9
- ISSN:
- 0305-1048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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