The physical realization of scalable quantum processors is primarily limited by decoherence, which typically requires cryogenic conditions and complex error-correction protocols. In this paper, we present AṆU ASTRA, a room-temperature optical quantum co-processor capable of coherently simulating up to 1000 physical qubits. Unlike purely discrete-variable quantum computers that rely on fragile unitary gates for universal computation, the ASTRA architecture operates via a hybrid measurement-based quantum computation (MBQC) framework. It leverages continuous-variable (CV) macroscopic light intensity, utilizing Time-Division Multiplexing (TDM) through active Liquid Crystal Variable Retarders (LCVRs) driven by a digital Real-Time Operating System (RTOS). This allows for the manipulation of coherent optical states across discrete temporal bins, effectively emulating large-scale entanglement graphs without the exponential hardware overhead or cryogenic footprint of superconducting systems. We empirically validate the architecture's statistical quantum non-determinism, passing NIST SP 800-90B entropy tests for True Quantum Random Number Generation (QRNG). Furthermore, the practical computational utility of the co-processor is demonstrated through the physical execution of the Quantum Approximate Optimization Algorithm (QAOA), Quantum Support Vector Machine (QSVM) non-linear classification, and Grover's algorithm amplitude amplification. This architecture provides a highly scalable, low-cost, room-temperature pathway for applied quantum optimization and machine learning tasks.
The physical realization of scalable quantum processors is primarily limited by decoherence, which typically requires cryogenic conditions and complex error-correction protocols. In this paper, we present AṆU ASTRA, a room-temperature optical quantum co-processor capable of coherently simulating up to 1000 physical qubits. Unlike purely discrete-variable quantum computers that rely on fragile unitary gates for universal computation, the ASTRA architecture operates via a hybrid measurement-based quantum computation (MBQC) framework. It leverages continuous-variable (CV) macroscopic light intensity, utilizing Time-Division Multiplexing (TDM) through active Liquid Crystal Variable Retarders (LCVRs) driven by a digital Real-Time Operating System (RTOS). This allows for the manipulation of coherent optical states across discrete temporal bins, effectively emulating large-scale entanglement graphs without the exponential hardware overhead or cryogenic footprint of superconducting systems. We empirically validate the architecture's statistical quantum non-determinism, passing NIST SP 800-90B entropy tests for True Quantum Random Number Generation (QRNG). Furthermore, the practical computational utility of the co-processor is demonstrated through the physical execution of the Quantum Approximate Optimization Algorithm (QAOA), Quantum Support Vector Machine (QSVM) non-linear classification, and Grover's algorithm amplitude amplification. This architecture provides a highly scalable, low-cost, room-temperature pathway for applied quantum optimization and machine learning tasks.
The commercial viability of utility-scale quantum computation is fundamentally constrained by the decoherence thresholds of single-particle logic gates, necessitating complex dilution refrigeration and exponential hardware scaling. In this paper, we present a paradigm shift in continuous-variable quantum processing: the A\d{n}uAstra Chromatic Quantum Ontology (CQO). By redefining the qubit not as a fragile single particle, but as a multi-spectral wave-path manifold, quantum operations are mapped to macroscopic optical interference patterns that maintain coherence under ambient conditions. Furthermore, we introduce Recursive Spatio-Temporal Multiplexing via a Micro-Cavity Recirculation Loop to scale quantum circuit depth natively without expanding the physical hardware footprint. A working physical prototype was constructed and empirically validated for under ₹20,000 INR. The prototype successfully executed Deutsch's algorithm, Grover's search, QAOA, VQE, and BB84 protocols utilizing standard silicon detector arrays at room temperature (25°C), effectively eliminating the cryogenic bottleneck inherent in superconducting modalities.
Quantum computers, leveraging the principles of quantum mechanics, hold the potential to surpass classical computers in numerous applications, with implications across various domains. Besides the well-known gate model, Measurement-based Quantum Computation (MBQC) is another promising computational approach to achieve universal quantum computation. In MBQC, large ensembles of qubits are prepared in a highly entangled cluster state, forming the basis for executing quantum computations through sequential measurements. Cluster states are realized using both continuous variables (CV) and discrete variables (DV) techniques. In the CV-based methods, Frequency Domain Multiplexing (FDM), Time Domain Multiplexing (TDM), Spatial Domain Multiplexing (SDM), and hybrid schemes are employed. This paper thoroughly discusses and compares these approaches, elucidating their strengths and limitations. Additionally, the generation of photonic cluster states in DV is explored and some recent results are reported. Some recent state-of-the-art advancements in photonic and superconducting qubits entanglement, which can potentially serve as cluster states, are also presented. Finally, we highlight the approach that exhibits the most promising characteristics for achieving efficient cluster state realization in the context of MBQC.
Emerging post-quantum cryptographic (PQC) algorithms demand execution speeds beyond the capabilities of conventional CMOS accelerators. This work introduces a hybrid CMOS–superconducting quantum co-processor architecture that synergizes room-temperature CMOS logic with a cryogenic superconducting quantum accelerator to achieve ultra-fast PQC operations. A segmented pipeline allocates key generation and parameter management to CMOS units, while time-critical modular arithmetic and lattice-based transformations execute on a superconducting quantum processing core. Co-design optimizes interconnect latency, cryogenic interface protocols, and error-mitigation routines. Prototype evaluations demonstrate a 10× throughput improvement for key encapsulation mechanisms and a 20× reduction in energy per operation compared to state-of-the-art CMOS-only PQC accelerators. The hybrid platform supports flexible algorithm updates and mitigates decoherence through dynamic qubit refresh and adaptive error correction. Results indicate feasibility for real-time secure communications in next-generation networks.
The rapid advancement of quantum computation poses an existential threat to classical cryptographic protocols via Shor’s algorithm. Quantum Key Distribution (QKD), specifically the BB84 protocol, provides mathematically unconditional security but typically requires fragile, cryogenic single-photon sources. In this paper, we present empirical data demonstrating the flawless execution of the BB84 protocol and true Quantum Random Number Generation (QRNG) on the A\d{n}uAstra Chromatic Quantum Computer - a macroscopic, continuous-variable quantum computer operating entirely at ambient room temperature (25∘C). Our physical architecture achieved a 0.00% Quantum Bit Error Rate (QBER) during active polarization basis sifting. Furthermore, we introduce Physical Shear Detection, a novel hardware-level channel integrity metric that physically collapses eavesdropping attempts. These empirical benchmarks prove the commercial viability of macroscopic optical pathways for deployable, enterprise-grade, cryogenic-free quantum encryption.
This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.
One of the primary focuses in the rapidly changing scenery of quantum computing is one of the most promising methodologies for transgressing the current feat of quantum technologies: a better mix of quantum and classical paradigms of computations.This paper suggests a new hybrid quantum-classical processor architecture that augments the computational speed and enhances efficiency by utilizing powers emanating from the quantum and classical processing units.The architecture proposed in the study incorporates quantum processing units with classical processing units, with CPUs interfaced tightly to support dynamic task allocation with a view toward computational requirements.Hybrid processor design caters to the performance of quantum algorithms that usually involve classical preprocessing and postprocessing, such as the Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm.These algorithms are essential for any complicated optimization, the simulation of quantum systems, and applications in machine learning.We provide an insightful processor architecture design, understanding the communication protocol details between QPU and CPU, the scheduling mechanism for hybrid tasks, and the optimization techniques employed to minimize data transfer overhead while maximizing computational throughput.A lot of these implementation challenges, like quantum error correction, managing the coherence time, and classical-quantum data conversion, are something that innovations in hardware design and software algorithms can achieve.In this paper, the performance of the hybrid processor is, for the first time, fully benchmarked based on simulation and experimental setups.Resultsshow that this hybrid approach outperforms standalone quantum and classical processors on specific computational tasks by ten times in optimization problems and five times for machine learning.These include case studies showing that for some problems, the processor can solve real-world problems much more efficiently than any existing quantum or classical system.Such case studies include quantum chemistry simulations, cryptographic tasks, and training machine learning models.We turn to the scalability and flexibility of the hybrid processor to extend the horizon of these case studies and to bridge the gap between the current quantum computing capabilities and the race toward fully functional quantum computers.This new hybrid quantum-classical processor architecture makes significant progress in quantum computing by providing a pragmatic, workable solution for harnessing quantum mechanics but using the maturity of classical computing.The results show how to proceed with the research involving the hybrid system and the many potential applications of such systems across optimization, cryptography, and artificial intelligence.This study is significant as it offers a practical approach to overcoming the limitations of current quantum and classical processors, providing a pathway to more efficient and scalable computational solutions.