WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, announced the development of a binary string polynomial encoding for Quantum Random Access Memory (QRAM). Random Access Memory (RAM) is a crucial component in classical computing, enabling computers to quickly and randomly access stored data. In the context of quantum computing, QRAM is a type of memory that allows quantum computers to efficiently and parallelly access stored data without disrupting quantum states. QRAM is not only a core architecture for quantum data storage, but also a fundamental component for many quantum algorithms, such as the Grover search algorithm and Shor’s algorithm.
However, the process of quantum data access is far more complex than in classical computing. The nature of quantum states requires that data access preserves the superposition of the states while avoiding the introduction of measurement interference. As a result, designing an efficient QRAM architecture is highly challenging. Most existing QRAM designs are very costly in terms of computational resources (such as qubits, T gates, depth, etc.), making it difficult to implement large-scale applications on practical quantum computers.
WiMi has designed an entirely new QRAM architecture by introducing binary string polynomial encoding. In this design, Clifford+T circuits are utilized, and by optimizing the use of T gates, the efficiency of quantum circuits is significantly improved. Compared to the state-of-the-art QRAM bucket brigade architecture, this design has made significant breakthroughs in multiple key metrics.
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T-depth is one of the key metrics of quantum computing performance. The smaller the depth, the shorter the time required for the computational process, which in turn helps improve the overall efficiency of quantum algorithms. In this new QRAM design, we have achieved an exponential improvement in T-depth through polynomial encoding of binary strings. Specifically, in previous state-of-the-art bucket brigade QRAM architectures, the T-depth typically grows linearly with the number of memory locations, whereas WiMi has reduced the T-depth exponentially through polynomial encoding.
T-count is also a crucial optimization goal. T gates are expensive operations in quantum computing; their implementation not only consumes time but also depletes significant resources, especially in fault-tolerant quantum computing. To keep the T-count low, WiMi has adopted an innovative gate circuit optimization strategy in its design, ensuring that the T-count does not significantly increase while reducing the T-depth. Compared to previous state-of-the-art designs, this architecture maintains an asymptotically similar T-count. This means that, while the computational depth has been significantly reduced, the number of T gates required by the circuit has not increased drastically, ensuring efficient resource utilization.
Quantum bits (qubits) are the fundamental units of quantum computing and the core resource of a quantum computer. When designing a new QRAM architecture, optimizing other performance metrics while keeping the number of qubits constant has always been a significant challenge. WiMi has achieved a substantial improvement in qubit utilization efficiency through deep optimization of circuit design. In existing state-of-the-art designs, the number of qubits typically increases proportionally with the number of memory locations. However, in WiMi’s design, the same number of qubits is maintained while optimizing other computational resources (such as T-depth and T-count), resulting in a significant overall performance improvement.
WiMi’s binary string polynomial encoding for Quantum Random Access Memory (QRAM) also introduces the concept of a quantum Look-Up Table (qLUT). A qLUT, or Quantum Read-Only Memory (QROM), differs from traditional QRAM in that it has specific functional limitations. Specifically, QROM is a read-only structure, and the content it stores is fixed when the quantum state is initialized. Every time the memory content changes, the entire quantum circuit must be recompiled.
While the functionality of qLUT is limited, it shows extremely high efficiency in certain specific application scenarios. For example, when an algorithm requires frequent lookups of fixed, preset data, a qLUT can provide rapid data access at a lower computational cost. WiMi’s qLUT, combined with QRAM, further optimizes T-depth and T-count while maintaining a low qubit count, making it an extremely efficient data query tool in complex quantum algorithms.
WiMi‘s binary string polynomial encoding for Quantum Random Access Memory (QRAM) technology marks a significant leap in the performance of quantum computers. This technology not only brings deep theoretical optimizations but also provides strong practical support for various application scenarios, leading to a revolutionary improvement in the storage and access performance of quantum computers. Through significant optimizations in T-depth, T-count, and qubit count, this technology breaks through the limitations of traditional QRAM architectures, making the efficient implementation of quantum computing more feasible.
In the future, this technology is expected to demonstrate immense application potential across various fields. Particularly in quantum algorithms that require fast, large-scale data access, such as chemical molecular simulations, financial market predictions, cryptography decryption, and artificial intelligence, the optimized QRAM technology will bring new possibilities for quantum computing. Especially when combined with the quantum internet currently under development, the future quantum computing ecosystem will be more efficient, stable, and scalable. As quantum computing technology continues to advance, this QRAM design will further drive the large-scale application of quantum computers in real-world scenarios.
SOURCE: PRNewsWire