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From Algorithms to an Operating System in the Era of Finite Quantum Computing: A Journey in Multi-Platform Quantum Computing

by Mohamed Lefliti
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Current price ₹3,818.00
Original price ₹4,459.00
Original price ₹4,459.00
Original price ₹4,459.00
(-14%)
₹3,818.00
Current price ₹3,818.00

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Book cover type: Paperback
  • ISBN13: 9789999345439
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Eliva Press
  • Publisher Imprint: Eliva Press
  • Publication Date:
  • Pages: 58
  • Original Price: USD 45.5
  • Language: English
  • Edition: N/A
  • Item Weight: 91 grams
  • BISAC Subject(s): General

This book presents a comprehensive software framework designed to solve one of the most critical challenges in modern quantum computing: fragmentation and limited interoperability between different hardware platforms and software stacks. By bringing together four interconnected contributions, the author establishes a foundational infrastructure necessary to move quantum computing from experimental research to practical, scalable application in the NISQ (Noisy Intermediate-Scale Quantum) era. First, the work introduces a lightweight Translation Protocol that bridges the gap between major frameworks such as IBM's Qiskit and Google's Cirq. Using OpenQASM as a universal intermediate language, this protocol enables semantic-preserving circuit translation, allowing code to run across heterogeneous systems and laying the groundwork for a future Quantum Internet. Second, it proposes Q-FRAG, a hybrid quantum-classical algorithm optimized for real-time 3D spatial processing. By encoding geometric data into quantum states and leveraging superposition, Q-FRAG explores vast configuration spaces simultaneously, demonstrating how quantum parallelism can offer exponential advantages for graphics and simulation tasks. Third, the book develops a Meta-Algorithm for Quantum Neural Networks (QNNs). This approach utilizes amplitude encoding and circuit reuse to drastically reduce the number of qubits required, compressing classical data efficiently and making quantum machine learning feasible on hardware with limited resources.

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