The usual representation of quantum algorithms, limited to the process of solving the problem, is physically incomplete. We complete it in three steps: extending the representation to the process of setting the problem, relativizing the extended representation to the problem solver to whom the problem setting must be concealed, and symmetrizing the relativized representation for time reversal to represent the reversibility of the underlying physical process. The third steps projects the input state of the representation, where the problem solver is (...) completely ignorant of the setting and thus the solution of the problem, on one where she knows half solution. Completing the physical representation shows that the number of computation steps required to solve any oracle problem in an optimal quantum way should be that of a classical algorithm endowed with the advanced knowledge of half solution. (shrink)

Whole brain emulation aims to re-implement functions of a mind in another computational substrate with the precision needed to predict the natural development of active states in as much as the influence of random processes allows. Furthermore, brain emulation does not present a possible model of a function, but rather presents the actual implementation of that function, based on the details of the circuitry of a specific brain. We introduce a notation for the representations of mind state, mind transition functions (...) and transition update functions, for which elements and their relations must be quantified in accordance with measurements in the biological substrate. To discover the limits of significance in terms of the temporal and spatial resolution of measurements, we point out the importance of brain region and task specific constraints, as well as the importance of in-vivo measurements. We summarize further problems that need to be addressed. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

Relying on the universality of quantum mechanics and on recent results known as the “threshold theorems,” quantum information scientists deem the question of the feasibility of large‐scale, fault‐tolerant, and computationally superior quantum computers as purely technological. Reconstructing this question in statistical mechanical terms, this article suggests otherwise by questioning the physical significance of the threshold theorems. The skepticism it advances is neither too strong (hence is consistent with the universality of quantum mechanics) nor too weak (hence is independent of technological (...) contingencies). *Received June 2009; revised August 2009. †To contact the author, please write to: Department of History and Philosophy of Science, College of Arts and Sciences, Indiana University, Bloomington, IN 47405; e‐mail: [email protected]. (shrink)

Quantum computers are hypothetical quantum information processing (QIP) devices that allow one to store, manipulate, and extract information while harnessing quantum physics to solve various computational problems and do so putatively more efficiently than any known classical counterpart. Despite many ‘proofs of concept’ (Aharonov and Ben–Or 1996; Knill and Laflamme 1996; Knill et al. 1996; Knill et al. 1998) the key obstacle in realizing these powerful machines remains their scalability and susceptibility to noise: almost three decades after their conceptions, experimentalists (...) still struggle to maintain useful quantum coherence in QIP devices with more than a pair of qubits (e.g., Blatt and Wineland 2008). This slow progress has prompted debates on the feasibility of quantum computers, yet the quantum information community has dismissed the skepticism as “ideology” (Aaronson 2004), claiming that the obstacles are merely technological (Kaye et al. 2007, 240). In a recent paper (Hagar 2009) I’ve argued that such a skepticism with respect to the feasibility of quantum computers need not be deemed ideological at all, and that the aforementioned ‘proofs of concept’ are physically suspect. Using analogies from the foundations of classical statistical mechanics (SM), I’ve also argued that instead of active error correction, the appropriate framework for debating the feasibility of large–scale, fault–tolerant and computationally superior quantum computers should be the project of error avoidance: rather than trying to constantly ‘cool down’ the QIP device and prevent its thermalization, one should try to locate those regions in the device’s state space which are thermodynamically ‘abnormal’, i.e., those regions in the device’s state space which resist thermalization regardless of external noise. This paper is intended as a further contribution to the debate on the feasibility of large–scale, fault–tolerant and computationally superior quantum computers. Relying again on analogies from the foundations of classical SM, it suggests a skeptical conjecture and frames it in the ‘passive’, error avoidance, context.. (shrink)