Entropy computing: efficient optimization in an open quantum photonic system
We introduce entropy computing, a new approach toward optimization that involves conditioning a quantum reservoir to stabilize and sustain the ground state. We experimentally demonstrate entropy computing through a hybrid photonic-electronic system leverages photon qudits, which are encoded as probability amplitudes in the time-frequency domain, and integrates electronic interconnects to create an arbitrary Hamiltonian. Our platform effectively addresses non-convex continuous variable and combinatorial optimization problems, showcasing its potential for solving complex computational challenges.
Authors
- Irwin Huang
- Yong Meng Sua (ORCID: https://orcid.org/0000-0001-5533-1032)
- Milan Begliarbekov
- Sam Wu (ORCID: https://orcid.org/0000-0003-2594-7383)
- Yu‐Ping Huang (ORCID: https://orcid.org/0000-0001-8570-5963)
- Mohammad‐Ali Miri (ORCID: https://orcid.org/0000-0003-3247-4596)
- Nicholas Chancellor (ORCID: https://orcid.org/0000-0002-1293-0761)
- Uchenna Chukwu (ORCID: https://orcid.org/0000-0002-1311-3827)
- David Haycraft
- Lac Nguyen
- Carrie Spear
- Pranav Mahamuni
- Cesar Martinez-Delgado
- Russell Huffman
- Wesley Dyk
- Nick Vrahoretis
- Mark Campanelli
- Joey Rupert
Institutions
- Queens College, CUNY (US)
- Quantum Technology Sciences (United States) (US)
Publication Details
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1117/12.3045254
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00